How does this skill help with token efficiency?

It provides strategies for removing redundant instructions, consolidating prompts, and optimizing context usage to reduce costs without sacrificing output quality.

What are Prompt Engineering Patterns?

They are standardized techniques and structural frameworks used to communicate more effectively with Large Language Models to improve output accuracy, logic, and reliability.

Can I use these patterns for production applications?

Absolutely. These patterns are specifically designed to move prompts from experimental stages to stable, production-grade implementations that handle edge cases gracefully.

Does this skill support Few-Shot Learning?

Yes, it includes advanced patterns for example selection, semantic similarity retrieval, and balancing demonstrations within context window constraints.

Prompt Engineering Patterns

Name: Prompt Engineering Patterns
Author: HermeticOrmus

byHermeticOrmus

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데이터 과학 및 ML

Optimizes LLM performance and reliability through advanced prompt engineering techniques like few-shot learning and chain-of-thought reasoning.

소개

This skill provides a comprehensive framework for mastering production-grade prompt engineering within Claude Code, enabling developers to implement sophisticated patterns such as dynamic few-shot selection, structured reasoning traces, and iterative template optimization. By formalizing these architectural patterns, it helps developers reduce output inconsistency and token waste while maximizing the controllability and accuracy of LLM-powered applications.

주요 기능

Structured reasoning elicitation including Chain-of-Thought and Tree-of-Thought
System prompt architecture for establishing specialized AI roles and constraints
Dynamic few-shot learning with semantic example selection strategies
Modular template systems with variable interpolation and conditional logic
Automated error recovery and fallback instruction patterns
2 GitHub stars

사용 사례

Refining and debugging prompts that suffer from hallucinations or formatting errors
Building robust production LLM applications with high output consistency
Designing complex RAG systems that require precise context and source integration

소개

주요 기능

Structured reasoning elicitation including Chain-of-Thought and Tree-of-Thought
System prompt architecture for establishing specialized AI roles and constraints
Dynamic few-shot learning with semantic example selection strategies
Modular template systems with variable interpolation and conditional logic
Automated error recovery and fallback instruction patterns
2 GitHub stars

사용 사례

Refining and debugging prompts that suffer from hallucinations or formatting errors
Building robust production LLM applications with high output consistency
Designing complex RAG systems that require precise context and source integration